Fuzzy Rule Learning for Material Classification from Imprecise Data

Arnaud Grivet Sébert (1) , Jean-Philippe Poli (1)

1 LI3A (CEA, LIST) - Intelligence Artificielle et Apprentissage Automatique (CEA, LIST)

To address the problem of illicit substance detection at borders, we propose a complete method for explainable classification of materials. The classification is performed using imaprecise chemical data, which is quite rare in the literature. We follow a two-step workflow based on fuzzy logic induction. Firstly, a clustering approach is used to learn the suitable fuzzy terms of the various linguistic variables. Secondly, we induce rules for a justified classification using a fuzzy decision tree. Both methods are adaptations from classic ones to the case of imprecise data. At the end of the paper, results on simulated data are presented in the expectation of real data.

Mots clés: Fuzzy rules; Explainable material classification; Imprecise data; fuzzy decision tree; Clustering; Fuzzy partitioning; fuzzy logic; artificial intelligence; machine learning; online learning

Domaines: Intelligence artificielle [cs.AI]

Arnaud Grivet Sébert, Jean-Philippe Poli. Fuzzy Rule Learning for Material Classification from Imprecise Data. IPMU 2018: International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Jun 2018, Cadiz, Spain. pp.62-73, ⟨10.1007/978-3-319-91473-2_6⟩. ⟨cea-01838452⟩